# Metro Analytics Catalog

The Metro Analytics Catalog is a curated collection of ready-to-deploy AI models and analytics workflows optimized for Intel hardware.
Each use case includes pre-trained models, sample code, and documentation to accelerate development of Metro AI Suite solutions.

All models are hosted on [Hugging Face](https://huggingface.co/collections/Intel/metro-analytics-catalog) and optimized for inference with OpenVINO and DLStreamer on Intel CPUs, GPUs, and NPUs.

---

## Models

<style>
.hf-catalog-container {
    display: grid;
    grid-template-columns: repeat(auto-fill, minmax(300px, 1fr));
    gap: 1.5rem;
    margin: 1.5rem 0;
}
.hf-model-card {
    border: 1px solid #e0e0e0;
    border-radius: 8px;
    padding: 1.25rem;
    background: var(--pst-color-background, #fff);
    transition: box-shadow 0.2s, transform 0.2s;
}
.hf-model-card:hover {
    box-shadow: 0 4px 12px rgba(0,0,0,0.1);
    transform: translateY(-2px);
}
.hf-model-card h3 {
    margin: 0 0 0.75rem 0;
    font-size: 1.1rem;
}
.hf-model-card h3 a {
    color: var(--pst-color-link, #0071c5);
    text-decoration: none;
}
.hf-model-card h3 a:hover {
    text-decoration: underline;
}
.hf-model-card .description {
    color: var(--pst-color-text-muted, #666);
    font-size: 0.9rem;
    margin-bottom: 0.75rem;
    line-height: 1.4;
}
.hf-model-card .stats {
    display: flex;
    gap: 1rem;
    font-size: 0.8rem;
    color: var(--pst-color-text-muted, #888);
    flex-wrap: wrap;
}
.hf-model-card .stat {
    display: flex;
    align-items: center;
    gap: 0.25rem;
}
.hf-model-card .tags {
    display: flex;
    flex-wrap: wrap;
    gap: 0.4rem;
    margin-top: 0.75rem;
}
.hf-model-card .tag {
    background: rgba(0, 113, 197, 0.1);
    color: var(--pst-color-link, #0071c5);
    padding: 0.15rem 0.5rem;
    border-radius: 4px;
    font-size: 0.75rem;
}
.hf-loading {
    text-align: center;
    padding: 3rem;
    color: var(--pst-color-text-muted, #666);
}
.hf-error {
    text-align: center;
    padding: 2rem;
    color: #c00;
    background: #fff0f0;
    border-radius: 8px;
}
[data-theme="dark"] .hf-model-card {
    border-color: #444;
    background: var(--pst-color-background, #1a1a1a);
}
</style>

<div id="hf-catalog-root">
    <div class="hf-loading">Loading models from Hugging Face...</div>
</div>

<script>
(function() {
    const HF_COLLECTION_ID = "Intel/metro-analytics-catalog-69e1467ebd6ffb957a2f3fdf";
    const container = document.getElementById("hf-catalog-root");

    function formatNumber(num) {
        if (num === undefined || num === null) return "0";
        if (num >= 1000000) return (num / 1000000).toFixed(1) + "M";
        if (num >= 1000) return (num / 1000).toFixed(1) + "K";
        return num.toString();
    }

    function formatDate(dateStr) {
        if (!dateStr) return "";
        const date = new Date(dateStr);
        return date.toLocaleDateString("en-US", { month: "short", day: "numeric", year: "numeric" });
    }

    function createModelCard(model) {
        const modelId = model.id || model.modelId;
        const name = modelId.split("/").pop().replace(/-/g, " ").replace(/\b\w/g, c => c.toUpperCase());
        const url = `https://huggingface.co/${modelId}`;
        
        const tags = (model.tags || []).slice(0, 5).filter(t => 
            !["openvino", "license:other"].includes(t) && !t.startsWith("license")
        );

        return `
            <div class="hf-model-card">
                <h3><a href="${url}" target="_blank" rel="noopener">${name}</a></h3>
                <div class="description">${model.description || model.cardData?.description || "Analytics use case optimized for Intel hardware with OpenVINO."}</div>
                <div class="stats">
                    <span class="stat" title="Downloads">⬇ ${formatNumber(model.downloads)}</span>
                    <span class="stat" title="Likes">♥ ${formatNumber(model.likes)}</span>
                    ${model.lastModified ? `<span class="stat" title="Last updated">🔄 ${formatDate(model.lastModified)}</span>` : ""}
                </div>
                ${tags.length ? `<div class="tags">${tags.map(t => `<span class="tag">${t}</span>`).join("")}</div>` : ""}
            </div>
        `;
    }

    async function loadCatalog() {
        try {
            // Fetch the HuggingFace collection
            const response = await fetch(`https://huggingface.co/api/collections/${HF_COLLECTION_ID}`);
            if (!response.ok) throw new Error(`HTTP ${response.status}`);
            
            const collection = await response.json();
            const modelItems = (collection.items || []).filter(item => item.type === "model");

            if (modelItems.length === 0) {
                container.innerHTML = '<div class="hf-error">No models found in the catalog.</div>';
                return;
            }

            // Fetch detailed info for each model in parallel
            const modelPromises = modelItems.map(async (item) => {
                try {
                    const res = await fetch(`https://huggingface.co/api/models/${item.id}`);
                    if (res.ok) return await res.json();
                    return { id: item.id, description: item.description };
                } catch {
                    return { id: item.id, description: item.description };
                }
            });

            const models = await Promise.all(modelPromises);
            
            container.innerHTML = `
                <p style="margin-bottom: 1rem; color: var(--pst-color-text-muted, #666);">
                    Showing <strong>${models.length}</strong> model${models.length !== 1 ? "s" : ""} from Hugging Face
                </p>
                <div class="hf-catalog-container">
                    ${models.map(createModelCard).join("")}
                </div>
            `;
        } catch (error) {
            console.error("Failed to load HF catalog:", error);
            container.innerHTML = `
                <div class="hf-error">
                    Unable to load models from Hugging Face.<br>
                    <a href="https://huggingface.co/collections/${HF_COLLECTION_ID}" target="_blank">View catalog on Hugging Face →</a>
                </div>
            `;
        }
    }

    if (document.readyState === "loading") {
        document.addEventListener("DOMContentLoaded", loadCatalog);
    } else {
        loadCatalog();
    }
})();
</script>

---

## Getting Started

Each use case includes:

- **Pre-trained Models** — OpenVINO IR models optimized for Intel hardware
- **Sample Code** — DLStreamer pipelines and Python scripts for quick deployment
- **Documentation** — Setup instructions, configuration options, and deployment guides

Visit individual use case pages on Hugging Face for model downloads, code samples, and detailed documentation.

---

## Related Resources

- [DL Streamer](../../edge-ai-libraries/dlstreamer/index.md) — GStreamer-based media analytics framework
- [OpenVINO Toolkit](../../edge-ai-libraries/OpenVINO.md) — Model optimization and inference engine
- [Visual Pipeline and Platform Evaluation Tool](../../edge-ai-libraries/visual-pipeline-and-platform-evaluation-tool/index.md) — Benchmark visual analytics pipelines